Poster Open Access
David, Romain; Mabile, Laurence; Yahia, Mohamed; Cambon-Thomsen, Anne; Archambeau, Anne-Sophie; Bezuidenhout, Louise; Bekaert, Sofie; Bertier, Gabrielle; Bravo, Elena; Carpenter, Jane; Cohen-Nabeiro, Anna; Delavaud, Aurélie; De Rosa, Michele; Dollé, Laurent; Grattarola, Florencia; Murphy, Fiona; Pamerlon, Sophie; Specht, Alison; Tassé, Anne-Marie; Thomsen, Mogens; Zilioli, Martina
The RDA-SHARC (SHAring Reward & Credit) interest group is an interdisciplinary volunteer member-based group set up as part of RDA (Research Data Alliance) to unpack and improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Background and objectives of this group are reported here. Notably, one of the objectives is to promote the inclusion of data sharing activities in the research (& researchers) assessment scheme at national and European levels. To this aim, the RDA-SHARC-IG is developing two assessment grids using criteria to establish if data are compliant to the F.A.I.R principles (findable /accessible / interoperable / reusable) based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2018; and E.U.Guidelines*): 1/ The self-assessment grid to be used by a scientist as a ‘checklist’ to identify her/his own activities and to pinpoint the hurdles that hinder efficient sharing and reuse of his/her data by all potential users. 2/ The two-level grid (quick/extensive) to be used by the evaluator to assess the quality of the researcher/scientist sharing practice, over a given period, taking into account the means & support available over that period. Assessment criteria are classified according their importance with regards to FAIRness (essential / recommended / desirable) meanwhile good practices are recommended for critical steps. To implement a highly fair assessment of the sharing process, appropriate criteria must be selected in order to design optimal generic assessment grids. This process requires participation, time and input from volunteer scientists data producers/users from various fields.